Network Data on the Statistical Testbench

A New Method for Generating Realistic Null Data Exploiting Underlying Graph Structure with Application to EEG

E. Pirondini, A. Vybornova, M. Coscia, and D. Van De Ville

Technological and computational advances are making available large amounts of high-dimensional and rich-structured biomedical data, including brain images and signals. Acknowledging the network structure in our analyses opens a multitude of avenues in investigating “systems level” properties. For instance, computational neuroscience has boosted the interest in modeling and analyzing large datasets using concepts normally applied in networks and graph theories.

On the Need of Standards for Brain-Machine Interface Systems

R. Chavarriaga, C. Carey, C. Tom, B. Ash

The field of Brain-Machine Interfacing (BMI) is going through a very exciting period where the state-of- the-art in research is currently being tested on its intended end-users. Evidently, this translation from laboratory proof-of concepts to viable clinical and assistive solutions entails a large set of challenges. Furthermore, the possibility of deploying and commercializing BMI-based solutions requires researchers, manufacturers, and regulatory agencies to ensure these devices comply with well-defined criteria on their safety and effectiveness. In consequence, there is an increased interest on development of appropriate standards for BMI systems.

About BrainInsight

BrainInsight, the IEEE Brain Initiative eNewsletter, is a quarterly online publication, featuring practical and timely information and forward-looking commentary on neurotechnologies. BrainInsight describes recent breakthroughs in research, primers on methods of interests, or report recent events such as conferences or workshops.

Managing Editor

Ricardo Chavarriaga
Zurich University of Applied Science(ZHAW)
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